Systems and methods for programming and operating deep brain stimulation arrays

ABSTRACT

Systems, methods and apparatus for determining and/or programming stimulation settings for a pulse generator capable of steering current to a deep brain stimulation array are disclosed. Individual patient brain geometry and lead specific geometry data can be used to generate a maximum activation function curve. Optimization methods can be used to find stimulation settings that are as close as possible to achieving the value of the maximum activation function curve.

RELATED APPLICATION

The present application claims the benefit of U.S. ProvisionalApplication No. 62/240,644 filed Oct. 13, 2015, which is herebyincorporated herein in its entirety by reference.

STATEMENT REGARDING FEDERALLY SPONSERED RESEARH OR DEVELOPMENT

This invention was made with government support under NS081118 awardedby the National Institutes of Health. The government has certain rightsin the invention.

TECHNICAL FIELD

The present disclosure relates generally to brain stimulation, and moreparticularly to systems and methods for deep brain stimulation viaelectrode arrays.

BACKGROUND

Deep brain stimulation (DBS) is an effective surgical procedure for thetreatment of a number of neurological and neuropsychiatric disorders,including medication-refractory Parkinson's disease (PD), essentialtremor (ET), dystonia, and severe obsessive compulsive disorder. Theprocedure involves placing an electrode lead into a brain region tomodulate abnormal neuronal activity with various forms of pulsatileelectrical stimulation. Successful treatment can be characterized byboth symptom suppression and lack of side-effects. Such success requiresaccurate lead placement as well as spatially targeted stimulationsettings to avoid activating regions that elicit, for example, adversemotor, sensory, and/or cognitive side-effects for the patient.

Traditional designs of DBS leads (for example, the Medtronic Model3387/3389) use four cylindrical electrodes to deliver current in anomnidirectional fashion around the lead. An improvement to this designis to enable the steering of current delivery both along and around theDBS lead, via, for example, circumferentially-segmented electrodes. Sucha DBS array (DBSA) might have, for example, 32 electrodes arranged ineight rows of four electrodes each. These DBS arrays are especiallyuseful in cases of off-target DBS implants and for small orcomplex-shaped brain targets, such as the subthalamic nucleus or thepedunculopontine nucleus.

With the larger number of electrodes available in a DBSA lead,programming, operation and/or control challenges have emerged. Whilemanual testing of potential settings is feasible with traditional fourelectrode DBS leads, the number of possible combinations is unwieldy,and thus a need exists for efficient, effective, and safe methods ofoperating, controlling and/or programming DBSA settings, as conventionalapproaches to programming DBSAs have numerous drawbacks.

Conventional manual programming of DBSA settings works much like anoptometrist performing a vision examination. A clinician will manuallytest many stimulation settings and evaluate the patient's response toeach in order to determine the best one to use. This process can takehours within a single clinical visit and often several clinical visitsover the course of weeks to months to optimize.

Conventional feedback-based systems have embedded technology to log andanalyze patient response information or certain biomarkers (such asfeatures in brain waves) in order to inform and update stimulationconfigurations. Sometimes a rating/ranking system is in place todetermine the best configuration based on these responses/biomarkers.

Conventional brain mapping has been used by some researchers who havecompiled intraoperative microelectrode stimulation data and mapped themonto a human brain atlas. An efficacy probability map is thus created byassigning every location on the brain atlas a probability for deliveringtherapeutic stimulation. The probability assignment is based onoverlapping normal distributions with therapeutic stimulation sites atthe center of each distribution. Finally, the efficacy probability map(brain atlas) is nonlinearly warped onto the patient's pre-operative MRIand used to determine the electrode settings that may deliver the besttherapy. This approach is entirely based on empirical patient data.

Finally, conventional patient anatomy-based computational neuron modelscan be used to predict the best stimulating electrode settings formodulating a particular pathway or pathways within the brain. Atherapeutic target volume in the brain is segmented and reconstructedfrom the patient's MRI data. The volume is populated with simulatedmodel neurons and virtual stimulation is applied to them. The tissueenclosed by the activated neurons under virtual DBS is termed “volume oftissue activated” (VTA). Large numbers of simulations are run in orderto account for the different stimulating electrode configurations,neuron orientations and locations. The solutions for each simulation arestored in a large lookup table. Given a new target volume forstimulation, the pre-compiled database can be searched for the settingthat gives the most overlap between the solution VTA and the targetvolume.

In summary, conventional approaches for programming deep brainstimulation systems cannot scale well to DBSAs with more than a handfulof electrodes. Manual and feedback-based programming methods can betailored to the patient but can take too much time and resources toimplement effectively. Mapping methods are limited by the availabilityof a sufficiently rich source database and do not take the uniquestructure of a patient's brain tissue into account. Finally,conventional computational models require vast computational resourcesthat may not be present in a clinical setting.

SUMMARY

The present disclosure discusses model-based and objective systems andmethods for operating, controlling and/or programming DBSAs that arebased on individual patient data and are computationally efficient. Inembodiments, methods and systems can use the superposition of electricfields to optimize electrode configurations in programming, controllingand/or operating high-density deep brain stimulation DBSAs. This is incontrast to conventional approaches that rely on biophysically complexneuron models to determine optimal electrode configurations. The optimacomputed from this new approach can also be tailored to user-specificbattery power constraints.

In an embodiment, a method for determining a stimulation setting foreach electrode in a deep brain stimulation array having one or moreelectrodes involves receiving brain geometry data (for example, from anMM), receiving lead geometry data (for example, from a CT scan),generating from the brain and lead geometry data one or more grid pointsrepresenting a target tissue to be activated, calculating a maximumactivation function value for each of the one or more grid points, andthen performing a convex optimization method to determine a set ofstimulation settings for each electrode such that the actual activationfunction value for each grid point is as close to the maximum activationfunction value for the grid points of interest.

In embodiments, the convex optimization method can use maximumdeviation, linear programming, or quadratic programming to determine theoptimal settings.

The above summary is not intended to describe each illustratedembodiment or every implementation. The figures and the detaileddescription that follow more particularly exemplify these embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments may be more completely understood in consideration of thefollowing detailed description in connection with the accompanyingdrawings, in which:

FIG. 1 is a side view depicting a deep brain stimulation systemimplanted into the body of a patient, according to an embodiment.

FIG. 2 is a perspective view of a deep brain stimulation lead with fourelectrodes, according to an embodiment.

FIG. 3 is a perspective view of a deep brain stimulation lead with anarray of electrodes, according to an embodiment.

FIG. 4 is a flowchart depicting a method of generating stimulationsettings for a deep brain stimulation array, according to an embodiment.

FIG. 5 is a block diagram depicting a deep brain stimulation system anda programmer, according to an embodiment.

FIG. 6A is a block diagram depicting a programmer, according to anembodiment.

FIG. 6B is a block diagram depicting a pulse generator, according to anembodiment.

FIG. 7 is a simulated view of a discretized brain volume according to anembodiment.

FIG. 8 is a simulated view of the results of generating stimulationsettings according to embodiments.

While embodiments are amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to be limiting with respect to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the appended claims.

DETAILED DESCRIPTION

FIG. 1 depicts an exemplary DBS system 100 including a lead 102comprising one or more electrodes 104 implanted within the thalamus ofthe brain of a patient. Lead 102 is in electrical connection via anoptional extension 106 to a pulse generator 108. In embodiments, pulsegenerator 108 can be implanted or external, and DBS lead 102 can betargeted to any brain region.

FIG. 2 depicts a detailed view of lead 102 with four circumpolarelectrodes 104 a-d. FIG. 3 depicts a DBS array (DBSA) lead 202 with agreater number of electrodes 104 spaced radially and transversely alonglead 202. Leads 102 and 202 can include an insulating layer surroundingconductors (not shown) providing electrical connection from electrodes104 to pulse generator 108. Leads 102 and 202 are examples of suitableleads, and other configurations, arrangements and types of leads can beused in various embodiments discussed herein.

Embodiments of the present disclosure are directed to determining andproviding therapies by electrical stimulation of one or more electrodes104 by pulse generator 108. FIG. 4 is a flowchart depicting an overviewof a method by which pulse generator 108 can be programmed with settingsfor each electrode that provide maximum stimulation to one or moretarget regions while minimizing stimulation of side effect regions.Though this will be discussed in more detail below, including in thecontext of examples of related systems, hardware and other features, theoverview related to FIG. 4 can be helpful for appreciating variousfeatures individually and in combination.

At 402, brain geometry data unique to a specific patient (for exampleMRI data) can be combined with brain atlas data to generate a discretebrain geometry grid. Data regarding the configuration of the electrodes104 on the implanted lead 102 or 202 can be used to generate a finiteelement model of the electrode configuration at 404. At 406, anactivation function (AF) value at each of the grid points can becalculated and used to construct a theoretical Max Curve at 408. The MaxCurve represents the greatest likelihood for cellular depolarizationthat can be transferred to any grid point given a fixed amount ofcurrent generated by pulse generator 108. The Max Curve can be used ascriteria for convex optimization via one or more optimization methods(such as convex optimization in one embodiment) at 410 to determine howmuch energy to output to each electrode. The solutions can be comparedat 412, and device programming can be set and/or carried out at 414. Inother embodiments, more or fewer activities can be carried out, suchthat additional activities not depicted in FIG. 4 can be included, oractivities that are depicted can be omitted.

Embodiments can include systems of hardware and software adapted tocarrying out the activities depicted in FIG. 4 above. FIG. 5 depicts anexample system in which MRI data 520, brain atlas data 522 and leadconfiguration data 524 are used by a programmer device 502 and pulsegenerator 108 in order to provide optimally programmed therapeuticstimulation 532 to electrodes 104. The data sources 520, 522, and 524can be used by programmer device 502 to determine stimulation settings530 that are customized to the patient. In various embodiments, allthree data sources 520, 522, 524 can be used, or fewer than all threecan be used. Programmer device 502 can be electrically and/orcommunicatively coupled to or otherwise receive data from some or all ofdata sources 520, 522, 524. Pulse generator 108 directs therapeuticstimulation currents 532 according to the stimulation settings 530,which can be determined by programmer device 502 based at least in parton the data from data sources 520, 522, 524. In still other embodiments,data from different or additional data sources also or instead can becommunicated to programmer device 502 for consideration in determiningstimulation settings 530.

Pulse generator 108 is in wired or wireless communication withprogrammer device 502. Programmer device 502 can be a handheld device,laptop or desktop computer, server, tablet, cellular or smart phone orother computing device capable of communication with pulse generator108. Programmer device 502 can present a user interface adapted to allowa user, such as a clinician, patient, or researcher, to review, monitor,and update device data and settings. As can be seen in FIG. 6A,programmer device 502 can comprise a brain geometry engine 604, leaddata engine 606, discretization engine 608, and optimization engine 610.Engines 604, 606, 608, 610 can be software, firmware, hardware orcombinations thereof in embodiments and can comprise or be controlled,executed and/or coupled by a processor, such as a microprocessor, orother computing device. In still other embodiments, engines 604, 606,608, 610 can be different functions, routines, algorithms, functionalunits, of a processor or other device. Examples of the tasks, activitiesand other characteristics of engines 604, 606, 608, 610 are discussedbelow. Other components of programmer 502, including hardware andsoftware components, can be included but are not specifically depictedin FIG. 6A.

As can be seen in FIG. 6B, pulse generator 108 can comprise a processor620 and a memory 630. Memory 630 can include storage for stimulationsettings 530 for each of electrodes 104 present on lead 102 or 202.Stimulation settings 530 can be, for example, an array, hash table,dictionary, database or other data structure keyed to each electrode104. The value at each key can be the amount of current (for example, inmilliamps) to be directed to each electrode 104 during a therapy pulse.Stimulation settings 530 can also include other data items such as pulsetime, pulse interval, or current or voltage timing voltages for rampedor shaped pulses. Pulse generator 108 can also have a communicationengine, capable of wired or wireless communication with programmerdevice 502. Pulse generator 108 can also have one or more sensingengines capable of interpreting data sensed by electrodes 104 or viasensors within pulse generator 108 itself. Pulse generator 108 can havea stimulation engine 640 capable of independent current-controlledstimulation through each electrode 104 provided. In embodiments, pulsegenerator 108 can house any of brain geometry engine 604, lead dataengine 606, discretization engine 608, and optimization engine 610instead of or in addition to the engines in programmer 502.

In operation, pulse generator 108 can provide therapy to brain or othertissue by directing pulses of electrical current to each electrode 104based on the values stored in stimulation settings 530. In order todetermine optimum stimulation settings that maximize therapeutic valuewhile minimizing side effects, patient and lead specific data can becombined by programmer 502 in order to determine optimum stimulationsettings 530.

Returning now to FIG. 6A, the various engines of programmer 502 will bedescribed in more detail. Each of the engines can be adapted to performone or more of the activities of the method discussed regarding FIG. 4,for example.

Brain geometry engine 604 is adapted to receive brain geometry data andgenerate a three-dimensional (3D) rectangular grid of multiple layerscorresponding to the tissue that is the target of stimulation (i.e.,generate a discrete brain grid at 402 of FIG. 4). Brain geometry datacan include MRI data 520 which can be produced via magnetic resonanceimaging (MRI) of the patient. MRI data 520 can comprise susceptibilityweighted imaging (SWI) in one embodiment.

In embodiments, brain geometry engine 604 then can obtain coronal imagesfrom the MM data and use those images to contour the target tissue.Returning to the above example, brain geometry engine 604 can align theacquired SWI data to the anterior commissure (AC)-posterior commissure(PC) plane and slice it to produce serial coronal sections. Thesecoronal sections, or images, can be analyzed to identify those that spanthe target tissue and matched to places from a tissue atlas (forexample, a brain atlas 522) in order to extract the contours of thetarget tissue. Brain geometry engine 604 can then map the contours ontoa 3D rectangular grid of axon nodal locations (e.g., in both theafferent and efferent directions from thalamus) as can be seen in FIG.7. This is just one example, however, and those skilled in the art willappreciate that other MRI data and acquisition sequences, as well asother mapping techniques, can be used in other embodiments.

Lead data engine 606 is adapted to generate and store a finite elementmodel (FEM) of the voltage distribution resulting from electricalstimulation through each of electrodes 104 on lead 102 or 202 (i.e.,generate a finite element DBSA model at 404 of FIG. 4). The electrodesurfaces can be designated as boundary current sources, and the walls ofthe bulk tissue cylinder can be set to ground. The voltage distributionsresulting from electrical stimulation through the electrodes can becalculated, such as via the finite element method by solving Poisson'sequation. Stimulation then can be performed for each electrode 104. Inone embodiment, the stimulation comprises monopolar cathodic or anodicstimulation (e.g., at ±1 mA), with each of the electrodes acting as thecathode and then the anode (or vice-versa). Programmer device 502 cancalculate the finite element model, or the calculated data can beprovided to programmer device 502 by a user. Programmer device 502 canoptionally store finite element models for multiple forms of leads 102or 202.

Discretization engine 608 is configured to determine the maximum AFvalue possible at each point in the grid produced by brain geometryengine 604, and to construct the theoretical maximum curve also known asthe Max Curve (such as at 406 and 408 in FIG. 4). In one embodiment,discretization engine 608 first calculates the activation functionvalues at each grid point along the fiber direction. This can be done,for example, using the following formula in one embodiment:

$\frac{\partial^{2}V}{\partial x^{2}} = \frac{{V\left( {x + {\Delta\; x}} \right)} - {2{V(x)}} + {V\left( {x - {\Delta\; x}} \right)}}{\Delta\; x^{2}}$in which x is a position along the direction of the fibers, V is thevoltage value as a function of position, and Δx is the internodaldistance. In other words, the AF is the second spatial derivative of thevoltage within the target tissue at each grid point. This formulationfor the AF is just one example, however, and those skilled in the artwill appreciate that choice of other formulations can be made in otherembodiments. Notably, other functional derivatives can be used in placeof AF to represent depolarization at the grid points. This includesfirst spatial derivatives as well as second spatial derivatives thatincorporate multiple fiber directions. Points that overlap spatiallywith the DBSA can also be discarded.

The AF values for the remaining n points can be stored in a matrix. Inone embodiment, the matrix can comprise an m×n matrix C, where m is thetotal number electrodes, the i^(th) row contains the AF values resultingfrom stimulation through the i^(th) electrode alone, delivering −1 mAmonopolar cathodic current. An example of matrix C is:

$C = \begin{pmatrix}\nabla_{1,1}^{2} & \ldots & \nabla_{1,n}^{2} \\\vdots & \ddots & \vdots \\\nabla_{m,1}^{2} & \ldots & \nabla_{m,n}^{2}\end{pmatrix}$

Poisson's equation in electrostatics dictates that voltage distributionis proportional to the current, ∇·σ∇V=−I, where σ is the specificelectrical conductivity of the tissue and I is the current. From this,it is possible to derive that the current changes with the secondderivative of the voltage. Therefore, it can be shown that the AF valuesresulting from multiple voltage sources can be linearly superimposed. Tofind the maximum possible AF at a given grid point from any possibleelectrode configuration (subject to, for example, a 1 mA powerconstraint), the following thought experiment can be considered: Supposethere are n different categories of items that can be manufactured, eachwith the same cost but different profit margins. For a givenmanufacturing budget, the highest profit achievable occurs when theentire budget goes into manufacturing the most profitable item.Likewise, it can be readily shown that the highest possible AF valueachievable at each grid point is obtained when stimulating through asingle electrode using the maximum allowable current (e.g., 1 mA).Therefore the maximum value in each column (j) of the C matrix is thetheoretical maximum AF value possible for grid point j. The maximum AFvalues can be sorted in ascending order and arranged into a Max Curve.Each grid point represents the center of a membrane compartment and haspotential to be the point of initiation of an action potential. PositiveAF values are responsible for directly depolarizing the cell membraneand are considered here as potential initiation sites for actionpotential generation. Negative AF values represent directhyperpolarization of the cell membrane and thus can limit the likelihoodof generating action potentials.

Optimization engine 610 can use the Max Curve values calculated bydiscretization engine 608 as a goal to reach when determining optimumstimulation settings 530 (such as at 410 of FIG. 4). The AF values dueto a given electrode configuration can be denoted AF_(j)=C_(j) ^(T)I,where I is the n×1 vector of current through each electrode, n is thenumber of electrodes, and AF_(j) and C_(j) are the AF value and columnof C corresponding to grid point j. Together the AF values resultingfrom stimulation at each grid point can form another curve called theActual Curve. At each grid point j, the difference between the maximumAF value and the actual AF value from stimulation with I is given by thefollowing: AF_(j,max)−AF_(j)=C_(j,max)−C_(j) ^(T)I.

Optimization engine 608 can then attempt to minimize this discrepancy byone of a variety of optimization methods using well-posed problems,which have unique and global minimums. These optimization methods caninclude, but are not limited to, linear programming, quadraticprogramming, and maximum deviation. An example of each method is givenbelow.

In one embodiment, linear programming (LP) can be used to minimize thedifference between the Max Curve and the Actual Curve by solving:

minimize: Σ(C_(j,max)−C_(j) ^(T)I)

subject to: ΣI_(k)=1 and I_(k)≥0, for all k

In one embodiment, quadratic programming (QP) can be used to minimizethe square of the difference between the Max Curve and the Actual Curveby solving:

minimize: Σ(C_(j,max)−C_(j) ^(T)I)²

subject to: ΣI_(k)=1 and I_(k)≥0, for all k

In one embodiment, the maximum deviation (MD) between the Max Curve andthe Actual Curve can be used to solve the following convex optimizationproblem:

minimize: max_(j)(C_(j,max)−C_(j) ^(T)I)

subject to: ΣI_(k)=1 and I_(k)≥0, for all k

In the embodiments above, the equations can be constrained by assumingthat the sum of currents through all electrodes (ΣI_(k)) be equal to themaximum allowable current (e.g., 1 mA), and that the current througheach electrode I_(k) be greater than or equal to zero. It will beappreciated, however, that these constraints are arbitrarily defined andcan be adjusted as necessary. In embodiments, the constraints can bedefined in the context of using total current amplitudes that do notexpress stimulation-evoked side effects. In embodiments, the constraintscan be defined based on user-specific battery power constraints, forexample, by raising or lowering the total sum of the currents.

For example, in an embodiment, one or more side effect regions can beincorporated within the objective function so optimization engine 610can generate stimulation settings 530 that maximally stimulate a regionor regions of interest while minimally stimulating one or more sideeffect regions within the brain. This can be done by summing the sideeffect region(s) with the region(s) of interest. This sum hascoefficients that represent the relative weightings of the region(s) ofinterest and the side effect region(s). Optimization engine 610 canallow for the adjustment of the weighting as desired to increasestimulation of the region(s) of interest while minimizing side effects.Thus, this weighting-based programming approach requires only adjustinga single variable, which significantly reduces the complexity ofprogramming even below current clinical approaches. Notably, thissimplification still leverages the current steering capabilities of highdensity DBS arrays.

In embodiments, the constraints on the current through each electrode,I_(k), can be adjusted to support optimization of anodal as well ascathodal current by allowing I_(k) to be less than zero. This can enablebipolar or multi-polar combinations to be included as well.

FIG. 8 depicts example therapy configurations generated via each of theMD method 802, QP method 804, and LP method 806 for a device implantedin the thalamus of a non-human primate with a DBS array having 32electrodes. Each potential configuration 802, 804, 806 is a group ofstimulation settings 530 that can be provide to, or programmed into,pulse generator 108. In each configuration 802, 804, 806, each electrode1-32 is assigned a color corresponding to the amount of current assignedto it for each pulse. As can be seen in FIG. 8, in this case, the MDmethod 802 produces the most active contacts, whereas the LP method 806has the smallest number of contacts with all of the current beingsupplied to electrode 18.

If multiple solutions are generated, they can be compared to determinethe actual device settings to use (such as at 412 of FIG. 4). Inembodiments, optimization engine 610 can generate solutions for eachoptimization method and present them to the user of programmer device502 to choose the ideal therapy configuration for the patient. In otherembodiments, optimization engine 610 can select among the solutions andapply the therapy configuration directly (such as at 414 of FIG. 4). Theskilled artisan will appreciate that there are many possible criteriafor selecting a final solution. In some embodiments, the chosen solutionmight be the solution that activates the most (or the fewest)electrodes. In other embodiments, the chosen solution may be one thatavoids activating certain electrodes. In still other embodiments, thechosen solution may involve averaging the current values at eachelectrode across the potential solutions. Yet further embodiments mayuse the other criteria, such as power consumption, or a combination ofcriteria in order to choose a solution.

Embodiments of this disclosure present several advantages overconventional methods and systems. For example, embodiments allow forautomated programming of deep brain stimulation settings via tools thatare simple to implement and use. The programming can be achieved quicklyand efficiently and is thus amenable to deployment as part of a <1 hrclinical visit.

Embodiments also allow for full exploration of the potentialcapabilities of a DBS array that would be unable to be manuallynavigated. Embodiments provide definitive solutions to optimalstimulating electrode configurations and are software-based and notreliant on hardware sensing capabilities, or the continuous sensing ofnew patient data.

The disclosed embodiments are also patient-specific as opposed torelying on accumulated patient data to map out potential therapeutictargets for stimulation in a new patient. Accumulation of such patientdatabases is not feasible for those who do not have direct access tosuch data. Although stimulation targets for certain disorders (such asParkinson's disease and Essential tremor) are well established,therapeutic “hot spots” may be different from patient to patient andcannot be generalized. Embodiments use patient-specific data (forexample, MRI data) to identify the therapeutic target region, andmaximize the probability of neuronal activation based on theories ofcellular excitation.

Embodiments also do not rely on resource-heavy simulations. While it ispossible to empirically determine the volume of tissue activated fromvirtual stimulation of realistic neuron models, such an approach wouldhave to consider many different scenarios, including locations andorientations of the neurons with respect to the DBS array, as well asthe electrode combinations and current distributions to use.

The values and particular formulas given above are examples and shouldnot be read as limiting the scope of this disclosure. Those skilled inthe art will recognize that the above values and formulas may beadjusted as necessary depending on the implantable technology used andthe physical characteristics of the patient.

Thus, in an embodiment, a method for determining a stimulation settingfor each electrode in a deep brain stimulation array having one or moreelectrodes comprises receiving brain geometry data; receiving leadgeometry data; generating, from the brain geometry data and the leadgeometry data, one or more grid points representing a target tissue tobe activated; calculating a maximum activation function value for eachof the one or more grid points; and performing a convex optimizationmethod to determine a set of stimulation settings for each electrodesuch that an actual activation function value for each grid point is asclose to the maximum activation function value for the grid point.

In embodiments, the convex optimization method can minimize the maximumdeviation between the actual activation function value and the maximumactivation function value for each axonal grid point.

In embodiments, the convex optimization method can minimize the sum ofthe square of the differences between the actual activation functionvalue and the maximum activation function value for each grid point.

In embodiments, the convex optimization method can minimize the sum ofthe differences between the actual activation function value and themaximum activation function value for each axonal grid point.

Additionally, in an embodiment, the stimulation settings can beconfigured to avoid activation of non-target tissue such that the methodfurther comprises identifying one or more grid points representingnon-target tissue to be avoided when stimulating through the one or moreelectrodes; determining a set of stimulation settings for at least oneof the one or more electrodes such that an actual activation functionvalue for each of the one or more non-target tissue grid points is asclose as possible to the minimum activation function value calculatedfor each of the one or more grid points; and including the set ofstimulation settings for at least one of the one or more electrodes inthe providing to the pulse generator.

In embodiments, computing devices, microprocessors and other computer orcomputing devices discussed herein can be any programmable device thataccepts digital data as input, is configured to process the inputaccording to instructions or algorithms, and provides results asoutputs. In an embodiment, computing, processor and/or other suchdevices discussed herein can be, comprise, contain or be coupled to acentral processing unit (CPU) configured to carry out the instructionsof a computer program. Computing, processor and/or other such devicesdiscussed herein are therefore configured to perform basic arithmetical,logical, and input/output operations.

Computing, processor and/or other devices discussed herein can includememory. Memory can comprise volatile or non-volatile memory as requiredby the coupled computing device or processor to not only provide spaceto execute the instructions or algorithms, but to provide the space tostore the instructions themselves. In embodiments, volatile memory caninclude random access memory (RAM), dynamic random access memory (DRAM),or static random access memory (SRAM), for example. In embodiments,non-volatile memory can include read-only memory, flash memory,ferroelectric RAM, hard disk, floppy disk, magnetic tape, or opticaldisc storage, for example. The foregoing lists in no way limit the typeof memory that can be used, as these embodiments are given only by wayof example and are not intended to limit the scope of the invention.

In embodiments, the system or components thereof can comprise or includevarious engines, each of which is constructed, programmed, configured,or otherwise adapted, to autonomously carry out a function or set offunctions. The term “engine” as used herein is defined as a real-worlddevice, component, or arrangement of components implemented usinghardware, such as by an application specific integrated circuit (ASIC)or field-programmable gate array (FPGA), for example, or as acombination of hardware and software, such as by a microprocessor systemand a set of program instructions that adapt the engine to implement theparticular functionality, which (while being executed) transform themicroprocessor system into a special-purpose device. An engine can alsobe implemented as a combination of the two, with certain functionsfacilitated by hardware alone, and other functions facilitated by acombination of hardware and software. In certain implementations, atleast a portion, and in some cases, all, of an engine can be executed onthe processor(s) of one or more computing platforms that are made up ofhardware (e.g., one or more processors, data storage devices such asmemory or drive storage, input/output facilities such as networkinterface devices, video devices, keyboard, mouse or touchscreendevices, etc.) that execute an operating system, system programs, andapplication programs, while also implementing the engine usingmultitasking, multithreading, distributed (e.g., cluster, peer-peer,cloud, etc.) processing where appropriate, or other such techniques.Accordingly, each engine can be realized in a variety of physicallyrealizable configurations, and should generally not be limited to anyparticular implementation exemplified herein, unless such limitationsare expressly called out. In addition, an engine can itself be composedof more than one sub-engines, each of which can be regarded as an enginein its own right. Moreover, in the embodiments described herein, each ofthe various engines corresponds to a defined autonomous functionality;however, it should be understood that in other contemplated embodiments,each functionality can be distributed to more than one engine. Likewise,in other contemplated embodiments, multiple defined functionalities maybe implemented by a single engine that performs those multiplefunctions, possibly alongside other functions, or distributeddifferently among a set of engines than specifically illustrated in theexamples herein.

Various embodiments of systems, devices and methods have been describedherein. These embodiments are given only by way of example and are notintended to limit the scope of the invention. It should be appreciated,moreover, that the various features of the embodiments that have beendescribed may be combined in various ways to produce numerous additionalembodiments. Moreover, while various materials, dimensions, shapes,configurations and locations, etc. have been described for use withdisclosed embodiments, others besides those disclosed may be utilizedwithout exceeding the scope of the invention.

Persons of ordinary skill in the relevant arts will recognize that theinvention may comprise fewer features than illustrated in any individualembodiment described above. The embodiments described herein are notmeant to be an exhaustive presentation of the ways in which the variousfeatures of the invention may be combined. Accordingly, the embodimentsare not mutually exclusive combinations of features; rather, theinvention can comprise a combination of different individual featuresselected from different individual embodiments, as understood by personsof ordinary skill in the art. Moreover, elements described with respectto one embodiment can be implemented in other embodiments even when notdescribed in such embodiments unless otherwise noted. Although adependent claim may refer in the claims to a specific combination withone or more other claims, other embodiments can also include acombination of the dependent claim with the subject matter of each otherdependent claim or a combination of one or more features with otherdependent or independent claims. Such combinations are proposed hereinunless it is stated that a specific combination is not intended.Furthermore, it is intended also to include features of a claim in anyother independent claim even if this claim is not directly madedependent to the independent claim.

Any incorporation by reference of documents above is limited such thatno subject matter is incorporated that is contrary to the explicitdisclosure herein. Any incorporation by reference of documents above isfurther limited such that no claims included in the documents areincorporated by reference herein. Any incorporation by reference ofdocuments above is yet further limited such that any definitionsprovided in the documents are not incorporated by reference hereinunless expressly included herein.

For purposes of interpreting the claims for the present invention, it isexpressly intended that the provisions of Section 112, sixth paragraphof 35 U.S.C. are not to be invoked unless the specific terms “means for”or “step for” are recited in a claim.

The invention claimed is:
 1. A method for determining a stimulationsetting for each electrode in a deep brain stimulation array includingone or more electrodes, the method comprising: using brain geometry datato generate a discrete brain geometry grid; identifying one or more gridpoints in the discrete brain geometry grid, the one or more grid pointsrepresenting a target tissue to be activated by stimulation through theone or more electrodes; calculating a maximum activation function valuefor each of the one or more grid points using an estimated voltagedistribution of the stimulation of the one or more electrodes;constructing a maximum curve for the discrete brain geometry grid fromthe maximum activation function value of each of the one or more gridpoints; optimizing a set of stimulation settings for each of the one ormore electrodes using one or more optimization methods such that anactual activation curve for the discrete brain geometry grid is as closeas possible to the maximum curve of the discrete brain geometry grid;and providing, to a pulse generator capable of delivering therapy viaeach of the one or more electrodes, the set of stimulation settings foreach electrode.
 2. The method of claim 1, wherein the identifyingcomprises: receiving brain geometry data; receiving lead geometry data;and generating, from the brain geometry data and the lead geometry data,the one or more grid points representing the target tissue to beactivated.
 3. The method of claim 1, wherein the optimizing comprisesminimizing a maximum deviation between the maximum activation functionvalue and the actual activation function value for each of the one ormore grid points.
 4. The method of claim 1, wherein the optimizingcomprises minimizing a difference between the maximum activationfunction value and the actual activation function value for each of theone or more grid points.
 5. The method of claim 1, wherein theoptimizing comprises minimizing a square of a difference between themaximum activation function value and the actual activation functionvalue for each of the one or more grid points.
 6. The method of claim 1,wherein the stimulation settings comprise electrical pulse amplitudes.7. The method of claim 1, wherein the stimulation settings compriseelectrical pulse widths.
 8. The method of claim 1, wherein theoptimizing comprises using an optimization routine.
 9. The method ofclaim 1, further comprising optimizing, using a plurality ofoptimization routines, a plurality of sets of stimulation settings foreach electrode.
 10. The method of claim 1, wherein the stimulationsettings are configured to avoid activation of non-target tissue suchthat the method further comprises: identifying one or more grid pointsrepresenting non-target tissue to be avoided when stimulating throughthe one or more electrodes; optimizing a set of stimulation settings foreach of the one or more electrodes such that an actual activationfunction value of the one or more grid points is as close as possible tothe minimum activation function value calculated for each of the one ormore grid points; and including the set of stimulation settings for atleast one of the one or more electrodes in the providing to the pulsegenerator.